Epidemic timecourse from simulations vs data

National

Using the final parameter values from each chain

**Fig. 1 ** Model calibration to incident cases (A) and incident deaths (B) reported by JHU CSSE for each state at 0.55% IFR assumptions, summed over entire country. (C) [Optional] Model comparison to hospitalizations (not fit). Shaded areas show 95% confidence intervals based on 4independent inference runs and black points/lines indicate data reported by JHU CSSE.

Fig. 1 Model calibration to incident cases (A) and incident deaths (B) reported by JHU CSSE for each state at 0.55% IFR assumptions, summed over entire country. (C) [Optional] Model comparison to hospitalizations (not fit). Shaded areas show 95% confidence intervals based on 4independent inference runs and black points/lines indicate data reported by JHU CSSE.

State-level

(for subset of states only now)

**Fig. 2** Calibration of estimated incident cases and deaths to reported data from JHU CSSE, and validation of estimates for occupied hospital beds when compared to CDPH data. Here, modeled cases are calculated as a percent of modeled infection that is fit to county data. Black points represent actual data, lines represent means and shading represents the 95% prediction interval for each scenario at 0.55% IFR and 0.5% assumptions. Note that JHU CSSE data were reported as daily cumulative cases and deaths. In this figure, daily cumulative case counts were differenced in order to report the incident cases and deaths. **In comparing the actual and modeled data, we emphasize that limited testing and reporting delays may affect the quality of the reported case data early on in the outbreak.**

Fig. 2 Calibration of estimated incident cases and deaths to reported data from JHU CSSE, and validation of estimates for occupied hospital beds when compared to CDPH data. Here, modeled cases are calculated as a percent of modeled infection that is fit to county data. Black points represent actual data, lines represent means and shading represents the 95% prediction interval for each scenario at 0.55% IFR and 0.5% assumptions. Note that JHU CSSE data were reported as daily cumulative cases and deaths. In this figure, daily cumulative case counts were differenced in order to report the incident cases and deaths. In comparing the actual and modeled data, we emphasize that limited testing and reporting delays may affect the quality of the reported case data early on in the outbreak.

Correlations between fit parameter values (identifiability analysis)

Correlations are for within a particular MCMC chain, treating each iteration as a sample

For each state individually

## [1] "California"

## [1] "Florida"

## [1] "Illinois"

## [1] "NewYork"

## [1] "Ohio"

## [1] "Texas"

## [1] "Washington"

Average over all states

Variance of posterior vs proposal step size

This is a way of measuring whether adaptive MCMC could help. (Ignore covariance for now). If observed std >> perturb_sd, suggests that proposal too small. If observed_sd << perturb_sd, suggests proposal too big. Done for each parameteres

Final global likelihood values

#OTHER

Example code to read in SEIR values directly